How the sausage is made: Equity as a Technical Skill in practice
Every September, organizations head back into planning mode. Strategies are refreshed, budgets are checked, and leaders ask, what needs to change this year?
At QuakeLab, we think this is the perfect moment to talk about how equity work actually gets done. Not in values statements or training sessions, but in the everyday design of systems, policies, and services. We call this Equity as a Technical Skill.
Here’s a behind-the-scenes look at how we approach it, both inside organizations (as an employer) and outside organizations (through the services and products they provide).
Inside the organization: Fixing policies and processes
The first step is choosing where to focus. Inside organizations, the best candidates are the policies and processes that shape people’s daily experience: hiring, promotions, performance reviews, scheduling, or expense reimbursements. Selection is not only about what feels inefficient or causes frustration. It is also about areas where inequity is already well-documented in research. For example, performance management systems have been shown to undervalue the contributions of women and racialized employees, mobility pathways often overlook employees with caregiving responsibilities, and pay structures consistently reflect gaps across gender and race. These are processes that look routine on the surface, but research shows how they quietly widen inequities when left unexamined. Choosing one process to examine deeply will always be more effective than spreading attention across many.
The work then starts with curiosity. What is this process supposed to achieve, and who is it supposed to serve? Defining the purpose of a policy or process helps us test whether it is delivering on its promise or simply reinforcing old habits. Alongside purpose, we identify the assumptions baked into the design. For example, hiring processes often assume that references are reliable indicators of future performance, when in practice they may say more about someone’s existing network than their ability to do the job.
Once purpose and assumptions are clear, we map the process. We chart every step, decision point, and handoff. Then we find the friction. This step focuses on inequity, not only on workload or efficiency. We ask where the process creates barriers, who is excluded by requirements, and whose time, money, or wellbeing is disproportionately affected.
At this stage, it is also essential to ask: who needs to be in the room? Are the people most affected by this process included in redesign conversations? Are the staff who actually implement the process present to identify practical constraints? Equity requires participation from both groups.
Only then do we move into design. We decide where to start, focusing first on the group most negatively affected. We define success with clear, measurable outcomes. We test a small change, learn from it, and measure the result against our criteria.
Example: Expense reimbursement
Purpose: Enable staff to spend money on behalf of the organization without personal financial strain.
Assumptions: Everyone can afford to float costs and wait weeks to be paid back.
Problem: Newcomers and early-career staff face real hardship when covering expenses.
Change: Introduce small cash advances and allow direct vendor payments.
Measure: Track out-of-pocket amounts and days to reimbursement by role and tenure.
Result: Reduced financial stress, faster turnaround, and smoother projects.
Outside the organization: redesigning services and products
The same logic applies to external services and products. Choosing where to focus starts with asking: where do people experience the most frustration, exclusion, or risk? Look for patterns in complaints, repeated requests, or high dropout rates. These are often signals of inequity built into the design. A service does not need to be completely overhauled to be improved. Choosing one entry point where inequity is most visible can shift outcomes for entire groups of users.
We begin by clarifying purpose and assumptions. A community clinic, for example, might define its purpose as “providing accessible care to residents.” Assumptions often sneak in, such as patients being able to navigate online booking, having a family doctor, or seeing a walk-in model as equally fair to all.
We then map the journey, capturing every step a person takes to access the service. From there, we find the break points, identifying where the service fails first and for whom. This step requires looking closely at how the design of the service reproduces inequities.
We ask: who needs to be part of this redesign? Are patients who experience barriers involved in shaping the fix? Are staff who deliver the service included to identify operational realities?
Next, we prioritize by choosing a group to focus on first, usually the one facing the greatest harm. We define success for that group, design a targeted change, and measure outcomes with disaggregated data.
Example: community clinic intake
Purpose: Ensure all residents can access timely primary care.
Assumptions: Patients already have a doctor, or can wait weeks for an appointment.
Problem: New patients wait too long to book, and face long walk-in lines.
Change: Reserve daily slots for new patients and add simple SMS triage questions.
Measure: Appointment wait times and walk-in congestion, broken down by patient type.
Result: Faster access for new patients, reduced backlog in walk-ins.
We have intentionally written this process in accessible terms because equity work should not feel out of reach. It is important to recognize that friction and inequity are not always obvious. Without the right expertise, organizations risk redesigning only for smoother functionality, while leaving deeper inequities untouched.
This is where professional support matters. At QuakeLab, we bring data, research, and tested frameworks to identify inequities that may otherwise be invisible. We help organizations avoid surface fixes and instead course-correct the systems that quietly disadvantage certain groups.
Try this in an hour:
Pick one process or service.
Define its purpose and write down the assumptions behind it.
Identify where inequity shows up.
Ask who should be involved in redesigning this process.
Define three clear measures of success.
Choose one small change you can test in two weeks.
Track outcomes by group, not just overall averages.
Think of your organization like a machine that has been running for years. You can oil the gears all you want, but if the gears were built to leave people out, the machine will keep producing inequity. This fall, we challenge you to pick one gear and rebuild it with intention. Start small, but start now. If you want a partner to run the experiment with you, invite us in for a “technical skill lab” and we will bring the data, the frameworks, and the practical tools to make the rebuild stick!
